XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.

https://www.tandfonline.com/doi/pdf/10.1080/00273171.2011.568786

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter.

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Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …

Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.

Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovas...

Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association

OBJECTIVE —In vitro evidence shows that immune function is compromised in people with diabetes. Although certain rare infections are more common and infection-related mortality is higher, the risk of acquiring an infectious disease for diabetic patients has never been quantified. RESEARCH DESIGN AND METHODS —A retrospective cohort study using administrative data compared all people with diabetes in Ontario, Canada, on 1 April 1999 to matched nondiabetic people ( n = 513,749 in each group). The risk ratios of having an infectious disease and of death attributable to infectious disease between those with and without diabetes were calculated. Secondary analysis individually examined common infectious diseases. The study was repeated using a second pair of cohorts defined in 1996 to confirm stability of the estimates. RESULTS —Nearly half of all people with diabetes had at least one hospitalization or physician claim for an infectious disease in each cohort year. The risk ratio for diabetic versus nondiabetic people was 1.21 (99% CI 1.20–1.22) in both cohort years. The risk ratio for infectious disease-related hospitalization was up to 2.17 (99% CI 2.10–2.23). The risk ratio for death attributable to infection was up to 1.92 (1.79–2.05). Many individual infections were more common in people with diabetes, especially serious bacterial infections. CONCLUSIONS —Diabetes confers an increased risk of developing and dying from an infectious disease, corroborating both in vitro evidence and commonly held clinical belief. In addition to microvascular and macrovascular sequelae, clinicians should consider infection a complication of diabetes.

https://care.diabetesjournals.org/content/diacare/26/2/510.full.pdf

Quantifying the Risk of Infectious Diseases for People With Diabetes

Propensity Score Methods Gave Similar Results to Traditional Regression Modeling in Observational Studies: A Systematic Review

OBJECTIVE—To determine whether women with gestational diabetes mellitus (GDM) have an increased risk of cardiovascular disease (CVD) following pregnancy.

RESEARCH DESIGN AND METHODS—All women aged 20–49 years with live births between April 1994 and March 1997 in Ontario, Canada, were identified. Women with GDM were matched with 10 women without GDM and were followed for CVD.

RESULTS—The matched cohorts included 8,191 women with GDM and 81,262 women without GDM. Mean age at entry was 31 years, and median follow-up was 11.5 years. The hazard ratio for CVD events was 1.71 (95% CI 1.08–2.69). After adjustment for subsequent type 2 diabetes, the hazard ratio was attenuated (1.13 [95% CI 0.67–1.89]).

CONCLUSIONS—Young women with GDM had a substantially increased risk for CVD compared with women without GDM. Much of this increased risk was attributable to subsequent development of type 2 diabetes.

https://care.diabetesjournals.org/content/diacare/31/8/1668.full.pdf

Increased risk of cardiovascular disease in young women following gestational diabetes mellitus.

OBJECTIVE—Diabetic patients with inadequate glycemic control ought to have their management intensified. Failure to do so can be termed “clinical inertia.” Because data suggest that specialist care results in better control than primary care, we evaluated whether specialists demonstrated less clinical inertia than primary care physicians. RESEARCH DESIGN AND METHODS—Using administrative data, we studied all non-insulin-requiring diabetic patients in eastern Ontario aged 65 or older who had A1c results >8% between September 1999 and August 2000. Drug intensification was measured by comparing glucose-lowering drug regimens in 4-month blocks before and after the elevated A1c test and was defined as 1) the addition of a new oral drug, 2) a dose increase of an existing oral drug, or 3) the initiation of insulin. Propensity score-based matching was used to control for confounding between groups. RESULTS—There were 591 patients with specialist care and 1,911 with exclusively primary care. In the matched cohorts, 45.1% of patients with specialist care versus 37.4% with primary care had drug intensification (P = 0.009). Most of this difference was attributed to specialists’ more frequent initiation of insulin in response to elevated A1c. CONCLUSIONS—Fewer than one-half of patients with high A1c levels had intensification of their medications, regardless of specialty of their physician. Specialists were more aggressive with insulin initiation than primary care physicians, which may contribute to the lower A1c levels seen with specialist care. Interventions assisting patients and physicians to recognize and overcome clinical inertia should improve diabetes care in the population.

https://care.diabetesjournals.org/content/diacare/28/3/600.full.pdf

Clinical inertia in response to inadequate glycemic control: do specialists differ from primary care physicians?

Objective To examine the risk of new onset diabetes among patients treated with different HMG-CoA reductase inhibitors (statins). Design Population based cohort study with time to event analyses to estimate the relation between use of particular statins and incident diabetes. Hazard ratios were calculated to determine the effect of dose and type of statin on the risk of incident diabetes. Setting Ontario, Canada. Participants All patients aged 66 or older without diabetes who started treatment with statins from 1 August 1997 to 31 March 2010. The analysis was restricted to new users who had not been prescribed a statin in at least the preceding year. Patients with established diabetes before the start of treatment were excluded. Interventions Treatment with statins. Main outcome measure Incident diabetes. Results Compared with pravastatin (the reference drug in all analyses), there was an increased risk of incident diabetes with atorvastatin (adjusted hazard ratio 1.22, 95% confidence interval 1.15 to 1.29), rosuvastatin (1.18, 1.10 to 1.26), and simvastatin (1.10, 1.04 to 1.17). There was no significantly increased risk among people who received fluvastatin (0.95, 0.81 to 1.11) or lovastatin (0.99, 0.86 to 1.14). The absolute risk for incident diabetes was about 31 and 34 events per 1000 person years for atorvastatin and rosuvastatin, respectively. There was a slightly lower absolute risk with simvastatin (26 outcomes per 1000 person years) compared with pravastatin (23 outcomes per 1000 person years). Our findings were consistent regardless of whether statins were used for primary or secondary prevention of cardiovascular disease. Although similar results were observed when statins were grouped by potency, the risk of incident diabetes associated with use of rosuvastatin became non-significant (adjusted hazard ratio 1.01, 0.94 to 1.09) when dose was taken into account. Conclusions Compared with pravastatin, treatment with higher potency statins, especially atorvastatin and simvastatin, might be associated with an increased risk of new onset diabetes.

https://www.bmj.com/content/bmj/346/bmj.f2610.full.pdf

Baiju R. Shah

Papers

Quantifying the Risk of Infectious Diseases for People With Diabetes

Propensity Score Methods Gave Similar Results to Traditional Regression Modeling in Observational Studies: A Systematic Review

Increased risk of cardiovascular disease in young women following gestational diabetes mellitus.

Clinical inertia in response to inadequate glycemic control: do specialists differ from primary care physicians?

Risk of incident diabetes among patients treated with statins: population based study